Gait-pattern adaptation algorithms based on neural network for lower limbs active orthoses

The this work deals with neural network-based gait-pattern adaptation algorithms for an active lower limbs orthosis. Stable trajectories are generated during the optimization process, considering a stable trajectory generator based on the Zero Moment Point criterion and the inverse dynamic model. Additionally, two neural network (NN) are used to decrease the time-consuming computation of the model and ZMP optimization. The first neural network approximates the inverse dynamics and the ZMP optimization, while the second one works in the optimization procedure, giving the adapting parameter according to orthosis-patient interaction. Also, a robust controller based on the ¿_¿ method is designed to attenuate the effects of external disturbances and parametric uncertainties in the trajectory tracking errors. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results.